Reduced false positive identification for spectroscopic quantification

ABSTRACT

A device may receive information identifying results of a spectroscopic measurement performed on an unknown sample. The device may determine a decision boundary for a quantification model based on a configurable parameter, such that a first plurality of training set samples of the quantification model is within the decision boundary and a second plurality of training set samples of the quantification model is not within the decision boundary. The device may determine a distance metric for the spectroscopic measurement performed on the unknown sample relative to the decision boundary. The device may determine a plurality of distance metrics for the second plurality of training set samples of the quantification model relative to the decision boundary. The device may provide information indicating whether the spectroscopic measurement performed on the unknown sample corresponds to the quantification model.

RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to U.S.Provisional Patent Application No. 62/622,641 filed on Jan. 26, 2018,the content of which is incorporated by reference herein in itsentirety.

BACKGROUND

Raw material identification may be utilized for quality-control ofpharmaceutical products. For example, raw material identification may beperformed on a medical material to determine whether componentingredients of the medical material correspond to a packaging labelassociated with the medical material. Similarly, raw materialquantification may be performed to determine a concentration of aparticular component of a particular sample. For example, raw materialquantification may be performed to determine the concentration of anactive ingredient in a medicine. Spectroscopy may facilitatenon-destructive raw material identification and/or quantification withreduced preparation and data acquisition time relative to otherchemometric techniques.

SUMMARY

According to some possible implementations, a device may include one ormore memories communicatively coupled to one or more processors. The oneor more memories and the one or more processors may be configured toreceive information identifying results of a spectroscopic measurementperformed on an unknown sample. The one or more memories and the one ormore processors may be configured to determine a decision boundary for aquantification model based on a configurable parameter, such that afirst plurality of training set samples of the quantification model iswithin the decision boundary and a second plurality of training setsamples of the quantification model is not within the decision boundary.The one or more memories and the one or more processors may beconfigured to determine a distance metric for the spectroscopicmeasurement performed on the unknown sample relative to the decisionboundary. The one or more memories and the one or more processors may beconfigured to determine a plurality of distance metrics for the secondplurality of training set samples of the quantification model relativeto the decision boundary. The one or more memories and the one or moreprocessors may be configured to determine whether the spectroscopicmeasurement performed on the unknown sample corresponds to thequantification model based on the distance metric for the spectroscopicmeasurement and the plurality of distance metrics for the secondplurality of training set samples. The one or more memories and the oneor more processors may be configured to provide information indicatingwhether the spectroscopic measurement performed on the unknown samplecorresponds to the quantification model.

According to some possible implementations, a non-transitorycomputer-readable medium may store one or more instructions. The one ormore instructions, when executed by one or more processors, may causethe one or more processors to obtain a quantification model relating toa particular type of material of interest. The quantification model maybe configured for determining a concentration of a particular componentin samples of the particular type of the material of interest. The oneor more instructions, when executed by the one or more processors, maycause the one or more processors to receive information identifying aresult of a particular spectroscopic measurement performed on an unknownsample. The one or more instructions, when executed by the one or moreprocessors, may cause the one or more processors to aggregate otherspectroscopic measurements of training set samples of the quantificationmodel into a single class for the quantification model. The one or moreinstructions, when executed by the one or more processors, may cause theone or more processors to subdivide the other spectroscopic measurementsof the training set samples into a first group and a second group. Thefirst group of the other spectroscopic measurements may be within adecision boundary. The second group of the other spectroscopicmeasurements may not be within the decision boundary. The one or moreinstructions, when executed by the one or more processors, may cause theone or more processors to determine that a metric for the particularspectroscopic measurement performed on the unknown sample relative tocorresponding metrics for the second group of the other spectroscopicmeasurements satisfies a threshold. The one or more instructions, whenexecuted by the one or more processors, may cause the one or moreprocessors to provide information indicating that the unknown sample isnot the particular type of the material of interest.

According to some possible implementations, a method may includereceiving, by a device, information identifying results of a nearinfrared (NIR) spectroscopic measurement performed on an unknown sample.The method may include determining, by the device, a decision boundaryfor a quantification model, wherein the decision boundary divides asingle class of the quantification model into a first plurality oftraining set samples of the quantification model within the decisionboundary and a second plurality of training set samples of thequantification model is not within the decision boundary. The method mayinclude determining, by the device, that a particular distance metricfor the NIR spectroscopic measurement performed on the unknown samplesatisfies a threshold relative to other distance metrics for the secondplurality of training set samples. The method may include providing, bythe device, information indicating that the NIR spectroscopicmeasurement performed on the unknown sample does not correspond to thequantification model based on determining that the particular distancemetric for the NIR spectroscopic measurement performed on the unknownsample satisfies the threshold relative to the other distance metricsfor the second plurality of training set samples.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are diagrams of an overview of an example implementationdescribed herein;

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented;

FIG. 3 is a diagram of example components of one or more devices of FIG.2;

FIG. 4 is a flow chart of an example process for generating aquantification model for spectroscopic quantification;

FIG. 5 is a diagram of an example implementation relating to the exampleprocess shown in FIG. 4;

FIG. 6 is a flow chart of an example process for avoidance of falsepositive identification during spectroscopic quantification; and

FIGS. 7A and 7B are diagrams of an example implementation relating tothe example process shown in FIG. 6.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Raw material identification (RMID) is a technique utilized to identifycomponents (e.g., ingredients) of a particular sample foridentification, verification, and/or the like. For example, RMID may beutilized to verify that ingredients in a pharmaceutical materialcorrespond to a set of ingredients identified on a label. Similarly, rawmaterial quantification is a technique utilized to perform aquantitative analysis on a particular sample, such as determining aconcentration of a particular component material in the particularsample. A spectrometer may be utilized to perform spectroscopy on asample (e.g., the pharmaceutical material) to determine components ofthe sample, concentrations of components of the sample, and/or the like.The spectrometer may determine a set of measurements of the sample andmay provide the set of measurements for a spectroscopic determination. Aspectroscopic classification technique (e.g., a classifier) mayfacilitate determination of the components of the sample orconcentrations of the components of the sample based on the set ofmeasurements of the sample.

However, some unknown samples, which are to be subject to aspectroscopic quantification, are not actually included in a class ofmaterials that a quantification model is configured to quantify. Forexample, for a quantification model trained to determine a concentrationof a particular type of protein in samples of fish, a user mayinadvertently provide a sample of beef for quantification. In this case,a control device may perform a spectroscopic quantification of thesample of beef, and may provide an identification of the sample of beefas having a particular concentration of the particular type of protein.However, because of differences between spectroscopic signatures of beefand fish and proteins thereof, the identification may be inaccurate, andmay be termed a false positive identification.

As another example, a quantification model may be trained to quantifyrelative concentrations of different types of sugar (e.g., glucose,fructose, galactose, and/or the like) and in unknown samples. However, auser of a spectrometer and a control device may inadvertently attempt toclassify an unknown sample of sugar based on incorrectly using thespectrometer to perform a measurement. For example, the user may operatethe spectrometer at an incorrect distance from the unknown sample, atenvironmental conditions different from calibration conditions at whichspectroscopy was performed to train the quantification model, and/or thelike resulting in an incorrectly obtained measurement. In this case, thecontrol device may receive an inaccurate spectrum for the unknown sampleresulting in a false positive identification of the unknown sample ashaving a first type of sugar at a first concentration, when the unknownsample actually includes a second type of sugar at a secondconcentration.

Some implementations, described herein, may use a single class supportvector machine (SC-SVM) technique to reduce a likelihood of falsepositive identification in spectroscopic quantification. For example, acontrol device that receives a spectroscopic measurement of an unknownsample may determine whether the spectroscopic measurement of theunknown sample corresponds to a class of materials that a spectroscopicmodel is configured to quantify. In some implementations, the controldevice may determine that the unknown sample is not associated with theclass of materials that the spectroscopic model is configured toquantify, and may provide information indicating that the unknown sampleis not associated with the class of materials, thereby avoiding a falsepositive identification of the unknown sample. Alternatively, based ondetermining that the unknown sample is associated with the class ofmaterials that the spectroscopic model is configured to quantify, thecontrol device may analyze a spectrum of the unknown sample to provide aspectroscopic determination, such as of a concentration, aclassification, and/or the like. Furthermore, the control device mayutilize confidence metrics, such as probability estimates, decisionvalues, and/or the like to filter out false positive identifications.

In this way, an accuracy of spectroscopy is improved relative tospectroscopy performed without identification of potential error samples(e.g., samples associated with a class of materials for which aspectroscopic model is not configured or samples for which aspectroscopic measurement is incorrectly obtained) and/or confidencemetrics. Moreover, a determination of whether a material is associatedwith a class for which a spectroscopic model is configured may be usedwhen generating a quantification model based on a training set of knownspectroscopic samples. For example, a control device may determine thata sample, of the training set, is not of a type corresponding to therest of the training set (e.g., based on human error resulting in anincorrect sample being introduced into the training set), and maydetermine not to include data regarding the sample when generating aquantification model. In this way, the control device improves anaccuracy of quantification models for spectroscopy.

FIGS. 1A and 1B are diagrams of an overview of an example implementation100 described herein. As shown in FIG. 1A, example implementation 100may include a control device and a spectrometer.

As further shown in FIG. 1A, the control device may cause thespectrometer to perform a set of spectroscopic measurements on atraining set and a validation set (e.g., a set of known samples utilizedfor training and validation of a classification model). The training setand the validation set may be selected to include a threshold quantityof samples for a component for which a quantification model is to betrained. Materials, in which the component may occur and which may beused to train the quantification model, may be termed materials ofinterest. In this case, the training set and the validation set mayinclude, for example, a first group of samples representing a firstconcentration of a material of interest, a second group of samplesrepresenting a second concentration of the material of interest, and/orthe like to enable training of a quantification model to identifyconcentrations of the material of interest in unknown samples.

As further shown in FIG. 1A, the spectrometer may perform the set ofspectroscopic measurements on the training set and the validation setbased on receiving an instruction from the control device. For example,the spectrometer may determine a spectrum for each sample of thetraining set and the validation set to enable the control device togenerate a set of classes for classifying an unknown sample as one ofthe materials of interest for the quantification model.

The spectrometer may provide the set of spectroscopic measurements tothe control device. The control device may generate a quantificationmodel using a particular determination technique and based on the set ofspectroscopic measurements. For example, the control device may generatea quantification model using a support vector machine (SVM) technique(e.g., a machine learning technique for information determination), suchas a single class SVM (SC-SVM) technique. The quantification model mayinclude information associated with assigning a particular spectrum to aparticular concentration of a component of a material of interest (e.g.,a particular level of concentration of the component in the material ofinterest). In this way, a control device can provide informationidentifying a concentration of a component in an unknown sample based onassigning a spectrum of the unknown sample to a particular class ofconcentration of the quantification model corresponding to a particularconcentration.

As shown in FIG. 1B, the control device may receive the quantificationmodel (e.g., from storage, from another control device that generatedthe quantification model, and/or the like). The control device may causea spectrometer to perform a set of spectroscopic measurements on anunknown sample (e.g., an unknown sample for which classification orquantification is to be performed). The spectrometer may perform the setof spectroscopic measurements based on receiving an instruction from thecontrol device. For example, the spectrometer may determine a spectrumfor the unknown sample. The spectrometer may provide the set ofspectroscopic measurements to the control device. The control device mayattempt to quantify the unknown sample based on the quantification model(e.g., classify the unknown sample into a particular class associatedwith a particular concentration or a particular quantity of a particularcomponent in the unknown sample). For example, the control device mayattempt to determine a particular concentration of ibuprofen within anunknown sample (e.g., of a pill), a particular quantity of units ofglucose within an unknown sample (e.g., of a sugar-based product),and/or the like.

With regard to FIG. 1B, the control device may attempt to determinewhether the unknown sample corresponds to the quantification model. Forexample, the control device may determine a confidence metriccorresponding to a likelihood that the unknown sample belongs to thematerial of interest (e.g., in any concentration of a set ofconcentrations for which the quantification model is configured usingthe training set and the validation set). As an example, for aquantification model configured to identify concentrations of ibuprofenwithin samples of ibuprofen pills, the control device may determinewhether the unknown sample is an ibuprofen pill (rather than anothertype of pill, such as an acetaminophen pill, an acetylsalicylic acidpill, and/or the like. As another example, for a quantification modelconfigured to identify concentrations of salt in a fish meat, thecontrol device may determine whether the unknown sample is fish meat(rather than chicken, beef, pork, and/or the like).

In this case, based on the control device determining that theconfidence metric, such as a probability estimate, a decision valueoutput of a support vector machine, and/or the like, satisfies athreshold (e.g., a standard deviation threshold as described herein),the control device may determine that the unknown sample is not amaterial of interest (e.g., which may correspond to the unknown samplebeing a different material, a spectroscopic measurement of the unknownsample being incorrectly performed, and/or the like). In this case, thecontrol device may report that the unknown sample cannot be accuratelyquantified using the quantification model, thereby reducing a likelihoodthat the unknown sample is subject to a false positive identification ofthe unknown sample as belonging to a particular concentration of acomponent in the material of interest.

In this way, the control device enables spectroscopy for an unknownsample with improved accuracy relative to other quantification modelsbased on reducing a likelihood of reporting a false positiveidentification of the unknown sample as being a particular concentrationof a component in the material of interest.

As indicated above, FIGS. 1A and 1B are provided merely as an example.Other examples are possible and may differ from what was described withregard to FIGS. 1A and 1B.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.2, environment 200 may include a control device 210, a spectrometer 220,and a network 230. Devices of environment 200 may interconnect via wiredconnections, wireless connections, or a combination of wired andwireless connections.

Control device 210 includes one or more devices capable of storing,processing, and/or routing information associated with spectroscopicquantification. For example, control device 210 may include a server, acomputer, a wearable device, a cloud computing device, and/or the likethat generates a quantification model based on a set of measurements ofa training set, validates the quantification model based on a set ofmeasurements of a validation set, and/or utilizes the quantificationmodel to perform spectroscopic quantification based on a set ofmeasurements of an unknown set. In some implementations, control device210 may utilize a machine learning technique to determine whether aspectroscopic measurement of an unknown sample is to be classified asnot corresponding to a material of interest for the quantificationmodel, as described herein. In some implementations, control device 210may be associated with a particular spectrometer 220. In someimplementations, control device 210 may be associated with multiplespectrometers 220. In some implementations, control device 210 mayreceive information from and/or transmit information to another devicein environment 200, such as spectrometer 220.

Spectrometer 220 includes one or more devices capable of performing aspectroscopic measurement on a sample. For example, spectrometer 220 mayinclude a spectrometer device that performs spectroscopy (e.g.,vibrational spectroscopy, such as a near infrared (NIR) spectrometer, amid-infrared spectroscopy (mid-IR), Raman spectroscopy, and/or thelike). In some implementations, spectrometer 220 may be incorporatedinto a wearable device, such as a wearable spectrometer and/or the like.In some implementations, spectrometer 220 may receive information fromand/or transmit information to another device in environment 200, suchas control device 210.

Network 230 may include one or more wired and/or wireless networks. Forexample, network 230 may include a cellular network (e.g., a long-termevolution (LTE) network, a 3G network, a code division multiple access(CDMA) network, etc.), a public land mobile network (PLMN), a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), a telephone network (e.g., the Public Switched Telephone Network(PSTN)), a private network, an ad hoc network, an intranet, theInternet, a fiber optic-based network, a cloud computing network, and/orthe like, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may beimplemented within a single device, or a single device shown in FIG. 2may be implemented as multiple, distributed devices. For example,although control device 210 and spectrometer 220 are described, herein,as being two separate devices, control device 210 and spectrometer 220may be implemented within a single device. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 200 may perform one or more functions described as beingperformed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to control device 210 and/or spectrometer 220. In someimplementations, control device 210 and/or spectrometer 220 may includeone or more devices 300 and/or one or more components of device 300. Asshown in FIG. 3, device 300 may include a bus 310, a processor 320, amemory 330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a wireless local area network interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for generating aquantification model for spectroscopic quantification. In someimplementations, one or more process blocks of FIG. 4 may be performedby control device 210. In some implementations, one or more processblocks of FIG. 4 may be performed by another device or a group ofdevices separate from or including control device 210, such asspectrometer 220.

As shown in FIG. 4, process 400 may include causing a set ofspectroscopic measurements to be performed on a training set and/or avalidation set (block 410). For example, control device 210 may cause(e.g., using processor 320, communication interface 370, and/or thelike) spectrometer 220 to perform a set of spectroscopic measurements ona training set and/or a validation set of samples to determine aspectrum for each sample of the training set and/or the validation set.The training set may refer to a set of samples of one or more knownmaterials with a set of concentrations of a component, which areutilized to generate a quantification model for the component.Similarly, the validation set may refer to a set of samples of one ormore known materials with a set of concentrations of the component,which are utilized to validate accuracy of the quantification model. Forexample, the training set and/or the validation set may include one ormore versions of a particular material (e.g., one or more versionsmanufactured by different manufacturers to control for manufacturingdifferences) in a set of different concentrations.

In some implementations, the training set and/or the validation set maybe selected based on an expected set of materials of interest for whichspectroscopic quantification is to be performed using the quantificationmodel. For example, when spectroscopic quantification is expected to beperformed for a pharmaceutical material to determine a concentration ofa particular component of the pharmaceutical material, the training setand/or the validation set may include a set of samples of the particularcomponent in a set of different possible concentrations in a set ofpharmaceutical materials that are to be tested for presence of theparticular component.

In some implementations, the training set and/or the validation set maybe selected to include a particular quantity of samples for eachconcentration of a material. For example, the training set and/or thevalidation set may be selected to include multiple samples (e.g., 5samples, 10 samples, 15 samples, 50 samples, etc.) of a particularconcentration. In this way, control device 210 can be provided with athreshold quantity of spectra associated with a particular type ofmaterial, thereby facilitating generation and/or validation of a class(e.g., a group of samples corresponding to a particular concentration ofthe component), for a quantification model, to which unknown samples canbe accurately assigned (e.g., based on unknown samples having theparticular concentration of the component).

In some implementations, control device 210 may cause multiplespectrometers 220 to perform the set of spectroscopic measurements toaccount for one or more physical conditions. For example, control device210 may cause a first spectrometer 220 and a second spectrometer 220 toperform a set of vibrational spectroscopic measurements using NIRspectroscopy. Additionally, or alternatively, control device 210 maycause the set of spectroscopic measurements to be performed at multipletimes, in multiple locations, under multiple different laboratoryconditions, and/or the like. In this way, control device 210 reduces alikelihood that a spectroscopic measurement is inaccurate as a result ofa physical condition relative to causing the set of spectroscopicmeasurements to be performed by a single spectrometer 220.

In this way, control device 210 causes the set of spectroscopicmeasurements to be performed on the training set and/or the validationset).

As further shown in FIG. 4, process 400 may include receivinginformation identifying results of the set of spectroscopic measurements(block 420). For example, control device 210 may receive (e.g., usingprocessor 320, communication interface 370, and/or the like) informationidentifying the results of the set of spectroscopic measurements. Insome implementations, control device 210 may receive informationidentifying a set of spectra corresponding to samples of the trainingset and/or the validation set. For example, control device 210 mayreceive information identifying a particular spectrum, which wasobserved when spectrometer 220 performed spectroscopy on the trainingset. In some implementations, control device 210 may receive informationidentifying spectra for the training set samples and the validation setsamples concurrently. In some implementations, control device 210 mayreceive information identifying spectra for the training set samples,may generate a quantification model, and may receive informationidentifying spectra for the validation set samples after generating thequantification model to enable testing of the quantification model.

In some implementations, control device 210 may receive the informationidentifying the results of the set of spectroscopic measurements frommultiple spectrometers 220. For example, control device 210 may controlfor physical conditions, such as a difference between the multiplespectrometers 220, a potential difference in a lab condition, and/or thelike, by receiving spectroscopic measurements performed by multiplespectrometers 220, performed at multiple different times, performed atmultiple different locations, and/or the like.

In some implementations, control device 210 may remove one or morespectra from utilization in generating the quantification model. Forexample, control device 210 may perform spectroscopic quantification andmay determine that a spectrum does not correspond to a type of materialfor which the quantification model is configured to quantify, and maydetermine that a sample corresponding to the spectrum was inadvertentlya material that is not of interest (e.g., based on human error incorrectly performing spectroscopy, errors in the information identifyingthe spectra of the training set, and/or the like). In this case, controldevice 210 may determine to remove the spectrum from the training set.In this way, control device 210 may improve an accuracy ofquantification models by reducing a likelihood that a quantificationmodel is generated using incorrect or inaccurate information regarding atraining set or validation set.

In this way, control device 210 receives information identifying resultsof the set of spectroscopic measurements.

As further shown in FIG. 4, process 400 may include generating aquantification model based on the information identifying the results ofthe set of spectroscopic measurements (block 430). For example, controldevice 210 may generate (e.g., using processor 320, memory 330, storagecomponent 340, and/or the like) a quantification model associated withan SVM classifier technique based on the information identifying theresults of the set of spectroscopic measurements.

SVM may refer to a supervised learning model that performs patternrecognition and uses confidence metrics for quantification. In someimplementations, control device 210 may utilize a particular type ofkernel function to determine a similarity of two or more inputs (e.g.,spectra) when generating a quantification model using the SVM technique.For example, control device 210 may utilize a radial basis function(RBF) (e.g., termed SVM-rbf) type of kernel function, which may berepresented as k(x,y)=exp(−∥x−y∥{circumflex over ( )}2) for spectra xand y; a linear function (e.g., termed SVM-linear and termedhier-SVM-linear when utilized for a multi-stage determination technique)type of kernel function, which may be represented as k(x,y)=<x·y>; asigmoid function type of kernel function; a polynomial function type ofkernel function; an exponential function type of kernel function; and/orthe like. In some implementations, control device 210 may generate thequantification model using a single class SVM (SC-SVM) classifiertechnique. For example, control device 210 may aggregate multipleclasses corresponding to multiple concentrations of a component in thetraining set to generate a single class representing the quantificationmodel. In this case, control device 210 may utilize a confidence metricto determine a likelihood that an unknown sample is of a type that thequantification model is configured to analyze, as described herein.

In some implementations, control device 210 may utilize a particulartype of confidence metric for SVM, such as a probability value based SVM(e.g., determination based on determining a probability that a sample isa member of a class (of concentration) of a set of classes (of possibleconcentrations)), a decision value based SVM (e.g., determinationutilizing a decision function to vote for a class, of a set of classes,as being the class of which the sample is a member), and/or the like.For example, during use of the quantification model with decision valuebased SVM, control device 210 may determine whether an unknown sample islocated within a boundary of a constituent class (e.g., a particularquantity or concentration of a component of the unknown sample) based ona plotting of a spectrum of the unknown sample, and may assign thesample to a class based on whether the unknown sample is located withinthe boundary of the constituent class. In this way, control device 210may determine whether to assign an unknown spectrum to a particularclass for quantification.

Although some implementations, described herein, are described in termsof a particular set of machine learning techniques, other techniques arepossible for determining information regarding an unknown spectrum, suchas a classification of the material and/or the like.

In some implementations, control device 210 may select the particularclassifier that is to be utilized for generating the quantificationmodel from a set of quantification techniques. For example, controldevice 210 may generate multiple quantification models corresponding tomultiple classifiers and may test the multiple quantification models,such as by determining a transferability of each model (e.g., an extentto which a quantification model generated based on spectroscopicmeasurements performed on a first spectrometer 220 is accurate whenapplied to spectroscopic measurements performed on a second spectrometer220), a large-scale determination accuracy (e.g., an accuracy with whicha quantification model can be utilized to concurrently identifyconcentrations for a quantity of samples that satisfy a threshold),and/or the like. In this case, control device 210 may select aclassifier, such as the SVM classifier (e.g., a hier-SVM-linearclassifier, an SC-SVM classifier, and/or the like), based on determiningthat the classifier is associated with superior transferability and/orlarge-scale determination accuracy relative to other classifiers.

In some implementations, control device 210 may generate thequantification model based on information identifying samples of thetraining set. For example, control device 210 may utilize theinformation identifying the types or concentrations of materialsrepresented by samples of the training set to identify classes ofspectra with types or concentrations of materials.

In some implementations, control device 210 may train the quantificationmodel when generating the quantification model. For example, controldevice 210 may cause the quantification model to be trained using aportion of the set of spectroscopic measurements (e.g., measurementsrelating to the training set). Additionally, or alternatively, controldevice 210 may perform an assessment of the quantification model. Forexample, control device 210 may validate the quantification model (e.g.,for predictive strength) utilizing another portion of the set ofspectroscopic measurements (e.g., the validation set).

In some implementations, control device 210 may validate thequantification model using a multi-stage determination technique. Forexample, for in-situ local modeling based quantification, control device210 may determine that a quantification model is accurate when utilizedin association with one or more local quantification models. In thisway, control device 210 ensures that the quantification model isgenerated with a threshold accuracy prior to providing thequantification model for utilization, such as by control device 210, byother control devices 210 associated with other spectrometers 220,and/or the like.

In some implementations, control device 210 may provide thequantification model to other control devices 210 associated with otherspectrometers 220 after generating the quantification model. Forexample, a first control device 210 may generate the quantificationmodel and may provide the quantification model to a second controldevice 210 for utilization. In this case, for in-situ local modelingbased quantification, the second control device 210 may store thequantification model (e.g., a global quantification model), and mayutilize the quantification model in generating one or more in-situ localquantification models for determining a concentration of a component ofa material in one or more samples of an unknown set. Additionally, oralternatively, control device 210 may store the quantification model forutilization by control device 210 in performing quantification, ingenerating one or more local quantification models (e.g., for in-situlocal modeling based quantification), and/or the like. In this way,control device 210 provides the quantification model for utilization inspectroscopic quantification of unknown samples.

In this way, control device 210 generates the quantification model basedon the information identifying the results of the set of spectroscopicmeasurements.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a diagram of an example implementation 500 relating to exampleprocess 400 shown in FIG. 4. FIG. 5 shows an example of generating aquantification model.

As shown in FIG. 5, and by reference number 505, control device 210-1transmits information to spectrometer 220-1 to instruct spectrometer220-1 to perform a set of spectroscopic measurements on training set andvalidation set 510. Assume that training set and validation set 510includes a first set of training samples (e.g., measurements of whichare utilized for training a quantification model) and a second set ofvalidation samples (e.g., measurements of which are utilized forvalidating accuracy of the quantification model). As shown by referencenumber 515, spectrometer 220-1 performs the set of spectroscopicmeasurements based on receiving the instruction. As shown by referencenumber 520, control device 210-1 receives a first set of spectra for thetraining samples and a second set of spectra for the validation samples.In this case, the training samples and the validation samples mayinclude samples of multiple concentrations of a component in a group ofmaterials of interest for quantification. For example, control device210-1 may receive spectra relating to generating a global model (e.g., aglobal classification model or quantification model) to identify a typeof meat using the global model and an in-situ local modeling technique(to generate a local model, such as a local classification model orquantification model), and to quantifying a concentration of aparticular protein in the type of meat. In this case, control device210-1 may be configured to generate multiple local quantification models(e.g., a first quantification model for determining the concentration ofthe particular protein in a first type of meat identified using in-situlocal modeling, a second quantification model for determining theconcentration of the particular protein in a second type of meatidentified using in-situ local modeling, and/or the like). Assume thatcontrol device 210-1 stores information identifying each sample oftraining set and validation set 510.

With regard to FIG. 5, assume that control device 210-1 has selected toutilize a hier-SVM-linear classifier for generating a classificationmodel, and an SC-SVM classifier for the multiple quantification models.As shown by reference number 525, control device 210-1 trains a globalclassification model using the hier-SVM-linear classifier and the firstset of spectra and verifies the global classification model using thehier-SVM-linear classifier and the second set of spectra. Further,control device 210-1 trains and verifies multiple local quantificationmodels (e.g., a local quantification model corresponding to each classof the global classification model and/or each class of a localclassification model generated based on the global classificationmodel). Assume that control device 210-1 determines that thequantification models satisfies a validation threshold (e.g., has anaccuracy that exceeds the validation threshold). As shown by referencenumber 530, control device 210-1 provides the quantification models tocontrol device 210-2 (e.g., for utilization when performing aquantification on spectroscopic measurements performed by spectrometer220-2) and to control device 210-3 (e.g., for utilization whenperforming a quantification on spectroscopic measurements performed byspectrometer 220-3).

As indicated above, FIG. 5 is provided merely as an example. Otherexamples are possible and may differ from what was described with regardto FIG. 5.

In this way, control device 210 facilitates generation of aquantification model based on a selected classification technique (e.g.,selected based on model transferability, large-scale quantificationaccuracy, and/or the like) and distribution of the quantification modelfor utilization by one or more other control devices 210 associated withone or more spectrometers 220.

FIG. 6 is a flow chart of an example process 600 for avoidance of falsepositive identification during raw material quantification. In someimplementations, one or more process blocks of FIG. 6 may be performedby control device 210. In some implementations, one or more processblocks of FIG. 6 may be performed by another device or a group ofdevices separate from or including control device 210, such asspectrometer 220.

As shown in FIG. 6, process 600 may include receiving informationidentifying results of a set of spectroscopic measurements performed onan unknown sample (block 610). For example, control device 210 mayreceive (e.g., using processor 320, communication interface 370, and/orthe like) the information identifying the results of the set of NIRspectroscopic measurements performed on the unknown sample. In someimplementations, control device 210 may receive information identifyingresults of a set of spectroscopic measurements on an unknown set (e.g.,of multiple samples). The unknown set may include a set of samples(e.g., unknown samples) for which a determination (e.g., a spectroscopicquantification) is to be performed. For example, control device 210 maycause spectrometer 220 to perform the set of spectroscopic measurementson the set of unknown samples, and may receive information identifying aset of spectra corresponding to the set of unknown samples.

In some implementations, control device 210 may receive the informationidentifying the results from multiple spectrometers 220. For example,control device 210 may cause multiple spectrometers 220 to perform theset of spectroscopic measurements on the unknown set (e.g., the same setof samples), and may receive information identifying a set of spectracorresponding to samples of the unknown set. Additionally, oralternatively, control device 210 may receive information identifyingresults of a set of spectroscopic measurements performed at multipletimes, in multiple locations, and/or the like, and may quantify aparticular sample based on the set of spectroscopic measurementsperformed at the multiple times, in the multiple locations, and/or thelike (e.g., based on averaging the set of spectroscopic measurements orbased on another technique). In this way, control device 210 may accountfor physical conditions that may affect results of the set ofspectroscopic measurements.

Additionally, or alternatively, control device 210 may cause a firstspectrometer 220 to perform a first portion of the set of spectroscopicmeasurements on a first portion of the unknown set and may cause asecond spectrometer 220 to perform a second portion of the set ofspectroscopic measurements on a second portion of the unknown set. Inthis way, control device 210 may reduce a quantity of time to performthe set of spectroscopic measurements relative to causing all thespectroscopic measurements to be performed by a single spectrometer 220.

In this way, control device 210 receives the information identifying theresults of the set of spectroscopic measurements performed on theunknown sample.

As further shown in FIG. 6, process 600 may include determining whetherthe unknown sample corresponds to a quantification model (block 620).For example, control device 210 may attempt to determine (e.g., usingprocessor 320, memory 330, storage component 340, and/or the like)whether the unknown sample is a material for which the quantificationmodel is configured to quantify and/or includes a component, in thematerial, for which the quantification model is configured to quantify.

In some implementations, control device 210 may use an SC-SVM classifiertechnique to determine whether an unknown spectrum corresponds to thequantification model. For example, control device 210 may determine aconfigurable parameter value, nu, for using the SC-SVM technique. Theparameter value may correspond to a ratio of training set samples thatare determined to be within a decision boundary for the SC-SVM techniqueto training set samples that are determined to not be within thedecision boundary. In some implementations, control device 210 maydetermine the decision boundary based on the parameter value. In someimplementations, control device 210 may use a cross-validation procedureto set multiple possible decision boundaries, and may combine results ofusing the multiple possible decision boundaries (e.g., via averaging) todetermine whether the unknown spectrum corresponds to the quantificationmodel.

In some implementations, based on setting a decision boundary to satisfythe parameter value (e.g., for a parameter value of 0.5, setting thedecision value such that half of measurements of the training set arelocated within the decision boundary and half of measurements of thetraining set are located outside the decision boundary), control device210 may determine a decision value, which may correspond to a distancemetric from a measurement to the decision boundary. For example, controldevice 210 may determine a location on a set of axes for the spectrum ofthe unknown sample, and may determine a distance between the locationand a nearest point of the decision boundary. Although someimplementations, described herein, are described in terms of a graph ora set of axes, implementations described herein may be determinedwithout use of a graph or the set of axes, such as using anotherrepresentation of data relating to the unknown spectrum.

In some implementations, control device 210 may determine a decisionvalue for the unknown spectrum. For example, control device 210 maydetermine a distance from the unknown spectrum to the decision boundary.In some implementations, control device 210 may determine decisionvalues for other measurements located outside the decision boundary. Inthis case, control device 210 may determine a statistical metric torepresent a quantity of standard deviations of the decision value of theunknown spectrum relative to decision values for other measurementsoutside the decision boundary. For example, control device 210 maydetermine a log-normal standard deviation based on a log-normaldistribution, and may determine whether the standard deviation satisfiesa threshold (e.g., 1 standard deviation, 2 standard deviations, 3standard deviations, etc.). In this case, based on the measurement ofthe spectrum of the unknown sample being greater than a thresholdquantity of standard deviations from the decision boundary (e.g., 3standard deviations from the decision boundary) relative to othermeasurements outside the decision boundary, control device 210 maydetermine that the unknown sample does not correspond to thequantification model (block 620—NO). Alternatively, based on themeasurement being less than the threshold quantity standard deviationsfrom the decision boundary, control device 210 may determine that theunknown sample does correspond to the quantification model (block620—YES). Although described herein in terms of a particular statisticaltechnique and/or a particular threshold quantity of standard deviations,other statistical techniques and/or thresholds may be used.

In this way, control device 210 enables identification of unknownspectra differing from the quantification model by a threshold amountwithout having the quantification model trained using samples similar tothe unknown sample (e.g., also differing from training set samples ofthe material of interest by the threshold amount). Moreover, controldevice 210 reduces an amount of samples to be collected for generatingthe quantification model, thereby reducing cost, time, and computingresource utilization (e.g., processing resources and memory resources)relative to obtaining, storing, and processing other samples for toensure accurate identification of samples differing from a material ofinterest and/or concentrations thereof by a threshold amount.

In this way, control device 210 determines whether the unknown samplecorresponds to the quantification model.

As further shown in FIG. 6, based on determining that the unknown samplecorresponds to the quantification model (block 620—YES) process 600 mayinclude performing one or more spectroscopic determinations based on theresults of the set of spectroscopic measurements (block 630). Forexample, control device 210 may perform (e.g., using processor 320,memory 330, storage component 340, and/or the like) one or morespectroscopic determinations based on the results of the set ofspectroscopic measurements. In some implementations, control device 210may assign the unknown sample to a particular class (e.g., representinga particular concentration of a set of concentrations of a component ina material of interest).

In some implementations, control device 210 may assign the particularsample based on a confidence metric. For example, control device 210 maydetermine, based on a quantification model, a probability that aparticular spectrum is associated with each class of the quantificationmodel (e.g., each candidate concentration). In this case, control device210 may assign the unknown sample to the class (e.g., a particularconcentration) based on a particular probability for the class exceedingother probabilities associated with classes. In this way, control device210 determines a concentration of a component in a material of interestthat the sample is associated with, thereby quantifying the sample.

In some implementations, to perform in-situ local modeling, such as forquantification models with greater than a threshold quantity of classes,control device 210 may generate a local quantification model based onthe first determination. The local quantification model may refer to anin-situ quantification model generated using an SVM determinationtechnique (e.g., SVM-rbf, SVM-linear, etc. kernel functions; probabilityvalue based SVM, decision value based SVM, etc.; and/or the like) basedon confidence metrics associated with the first determination.

In some implementations, control device 210 may generate a localquantification model based on performing the first determination usingthe global classification model. For example, when control device 210 isbeing utilized to determine a concentration of a component in an unknownsample, and multiple unknown samples are associated with differentquantification models for determining the concentration of thecomponent, control device 210 may utilize the first determination toselect a subset of classes as local classes for the unknown sample, andmay generate a local quantification model associated with the localclasses for the unknown sample. In this way, control device 210 utilizeshierarchical determination and quantification models to improvespectroscopic classification. In this case, control device 210 maydetermine whether the unknown sample corresponds to the localquantification model based on determining a distance metric for theunknown sample relative to a subset of other measurements of the localquantification model.

As an example, when performing raw material identification to determinea concentration of a particular chemical in a plant material, where theplant material is associated with multiple quantification models (e.g.,relating to whether the plant is grown indoors or outdoors, in winter orin summer, and/or the like), control device 210 may perform a set ofclassification determinations to identify a particular quantificationmodel. In this case, the control device 210 may determine that the plantis grown indoors in winter based on performing a set of determinations,and may select a quantification model relating to the plant being grownindoors in winter for determining the concentration of the particularchemical. Based on selecting the quantification model, control device210 may determine that the unknown sample corresponds to thequantification model, and may quantify the unknown sample using thequantification model.

In some implementations, control device 210 may fail to quantify theunknown sample using the quantification model. For example, based on oneor more decision values or other confidence metrics failing to satisfy athreshold, control device 210 may determine that the unknown samplecannot be accurately quantified using the quantification model (block630—A). Alternatively, control device 210 may successfully quantify theunknown sample based on one or more decision values or other confidencemetrics satisfying a threshold (block 630—B).

In this way, control device 210 performs one or more spectroscopicdeterminations based on the results of the set of spectroscopicmeasurements.

As further shown in FIG. 6, based on determining that the unknown sampledoes not correspond to the quantification model (block 620—NO) or basedon a determination failure when performing the one or more spectroscopicdeterminations (block 630—A), process 600 may include providing outputindicating that the unknown sample does not correspond to thequantification model (block 640). For example, control device 210 mayprovide (e.g., using processor 320, memory 330, storage component 340,communication interface 370, and/or the like) information, such as via auser interface, indicating that the unknown sample does not correspondto the quantification model. In some implementations, control device 210may provide information associated with identifying the unknown sample.For example, based on attempting to quantify an amount of a particularchemical in a particular plant, and determining that an unknown sampleis not of the particular plant (but, instead, of another plant, such asbased on human error), control device 210 may provide informationidentifying the other plant. In some implementations, control device 210may obtain another quantification model, and may use the otherquantification model to identify the unknown sample based on determiningthat the unknown sample does not correspond to the quantification model.

In this way, control device 210 reduces a likelihood of providingincorrect information based on a false positive identification of theunknown sample, and enables error correction by a technician byproviding information to assist in determining that the unknown samplewas, for example, of the other plant rather than the particular plant.

In this way, control device 210 provides output indicating that theunknown sample does not correspond to the quantification model.

As further shown in FIG. 6, based on a classification success whenperforming the one or more spectroscopic determinations (block 630—B),process 600 may include providing information identifying aclassification relating to the unknown sample (block 650). For example,control device 210 may provide (e.g., using processor 320, memory 330,storage component 340, communication interface 370, and/or the like)information identifying a quantification relating to the unknown sample.In some implementations, control device 210 may provide informationidentifying a particular class for the unknown sample. For example,control device 210 may provide information indicating that a particularspectrum associated with the unknown sample is determined to beassociated with the particular class corresponding to a particularconcentration of a component in a material of interest, therebyidentifying the unknown sample.

In some implementations, control device 210 may provide informationindicating a confidence metric associated with assigning the unknownsample to the particular class. For example, control device 210 mayprovide information identifying a probability that the unknown sample isassociated with the particular class and/or the like. In this way,control device 210 provides information indicating a likelihood that theparticular spectrum was accurately assigned to the particular class.

In some implementations, control device 210 may provide a quantificationbased on performing a set of classifications. For example, based onidentifying a local quantification model relating to a class of theunknown sample, control device 210 may provide information identifying aconcentration of a substance in an unknown sample. In someimplementations, control device 210 may update the quantification modelbased on performing a set of quantifications. For example, controldevice 210 may generate a new quantification model including the unknownsample as a sample of the training set based on determining aquantification of the unknown sample as a particular concentration of acomponent in a material of interest.

In this way, control device 210 provides information identifying theunknown sample.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

FIGS. 7A and 7B are diagrams of an example implementation 700 relatingto prediction success rates associated with example process 600 shown inFIG. 6. FIGS. 7A and 7B show example results of raw materialidentification using a hierarchical support vector machine(hier-SVM-linear) based technique.

As shown in FIG. 7A, and by reference number 705, control device 210 maycause spectrometer 220 to perform a set of spectroscopic measurements.For example, control device 210 may provide an instruction to causespectrometer 220 to obtain a spectrum for an unknown sample to determinea concentration of a component in the unknown sample. As shown byreference number 710 and reference number 715, spectrometer 220 mayreceive the unknown sample and may perform the set of spectroscopicmeasurements on the unknown sample. As shown by reference number 720,control device 210 may receive spectra for the unknown sample basedspectrometer 220 performing the set of spectroscopic measurements on theunknown sample.

As shown in FIG. 7B, control device 210 may use a quantification model725 to perform spectroscopic quantification. Quantification model 725includes a single class 730 determined based on a parameter value, nu,such that a decision boundary of the single class 730 results in athreshold ratio of samples of a training set within a decision boundaryto samples of the training set not within the decision boundary. In thiscase, quantification model 725 may be associated with multiplesub-classes corresponding to multiple different concentrations of thecomponent in samples of the training set. As shown by reference numbers735 and 740, a spectrum of the unknown sample is determined to notcorrespond to the quantification model based on a standard deviationvalue (e.g., σ=3.2) for a distance of the sample to the decisionboundary satisfying a threshold (e.g., 3). As shown by reference number745, control device 210 provides output to client device 750 indicatingthat the unknown sample does not correspond to the quantification model,rather than providing a false positive identification of the unknownsample as a particular concentration of a component in a material ofinterest.

As indicated above, FIGS. 7A and 7B are provided merely as an example.Other examples are possible and may differ from what was described withregard to FIGS. 7A and 7B.

In this way, control device 210 reduces a likelihood of providing aninaccurate result of spectroscopy based on avoiding a false positiveidentification of an unknown sample as being a particular concentrationof a component in a material of interest for which a quantificationmodel is trained to identify.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, etc.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related itemsand unrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” and/or the like are intended to be open-ended terms. Further,the phrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

1. A device, comprising: one or more memories; and one or moreprocessors, communicatively coupled to the one or more memories, to:receive information identifying results of a spectroscopic measurementperformed on an unknown sample; determine a decision boundary for aquantification model based on a configurable parameter, such that afirst plurality of training set samples of the quantification model iswithin the decision boundary and a second plurality of training setsamples of the quantification model is not within the decision boundary;determine a distance metric for the spectroscopic measurement performedon the unknown sample relative to the decision boundary; determine aplurality of distance metrics for the second plurality of training setsamples of the quantification model relative to the decision boundary;determine whether the spectroscopic measurement performed on the unknownsample corresponds to the quantification model based on the distancemetric for the spectroscopic measurement and the plurality of distancemetrics for the second plurality of training set samples; and provideinformation indicating whether the spectroscopic measurement performedon the unknown sample corresponds to the quantification model.
 2. Thedevice of claim 1, wherein the one or more processors, when determiningwhether the spectroscopic measurement performed on the unknown samplecorresponds to the quantification model, are to: determine that thespectroscopic measurement does not correspond to the quantificationmodel; and wherein the one or more processors, when providinginformation indicating whether the spectroscopic measurement performedon the unknown sample corresponds to the quantification model, are to:provide information indicating that the spectroscopic measurement doesnot correspond to the quantification model.
 3. The device of claim 1,wherein the one or more processors, when determining whether thespectroscopic measurement performed on the unknown sample corresponds tothe quantification model, are to: determine that the spectroscopicmeasurement does correspond to the quantification model; and wherein theone or more processors, when providing information indicating whetherthe spectroscopic measurement performed on the unknown samplecorresponds to the quantification model, are to: provide informationindicating that the spectroscopic measurement does correspond to thequantification model.
 4. The device of claim 1, wherein the one or moreprocessors, when determining whether the spectroscopic measurementperformed on the unknown sample corresponds to the quantification model,are to: determine a statistical metric for the distance metric relativeto the plurality of distance metrics; and determine whether thespectroscopic measurement performed on the unknown sample corresponds tothe quantification model based on the statistical metric.
 5. The deviceof claim 4, wherein the statistical metric is a log-normal standarddeviation; and wherein the one or more processors, when determiningwhether the spectroscopic measurement performed on the unknown samplecorresponds to the quantification model based on the statistical metric,are to: determine that the log-normal standard deviation satisfies athreshold; and determine whether the spectroscopic measurement performedon the unknown sample corresponds to the quantification model based ondetermining that the log-normal standard deviation satisfies thethreshold.
 6. The device of claim 1, wherein the quantification model isassociated with a single class support vector machine (SC-SVM)classifier.
 7. The device of claim 1, wherein the one or more processorsare further to: receive a plurality of spectroscopic measurementsrelating to the first plurality of training set samples and the secondplurality of training set samples; determine the quantification modelbased on the plurality of spectroscopic measurements; validate thequantification model based on another plurality of spectroscopicmeasurements of a plurality of validation set samples; store thequantification model; and where the one or more processors, whendetermining the decision boundary, are to: obtain the quantificationmodel from storage; and determine the decision boundary after obtainingthe quantification model from storage.
 8. A non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: obtain a quantificationmodel relating to a particular type of material of interest, thequantification model configured for determining a concentration of aparticular component in samples of the particular type of the materialof interest; receive information identifying a result of a particularspectroscopic measurement performed on an unknown sample; aggregateother spectroscopic measurements of training set samples of thequantification model into a single class for the quantification model;subdivide the other spectroscopic measurements of the training setsamples into a first group and a second group, the first group of theother spectroscopic measurements being within a decision boundary; thesecond group of the other spectroscopic measurements being not withinthe decision boundary; determine that a metric for the particularspectroscopic measurement performed on the unknown sample relative tocorresponding metrics for the second group of the other spectroscopicmeasurements satisfies a threshold; and provide information indicatingthat the unknown sample is not the particular type of the material ofinterest.
 9. The non-transitory computer-readable medium of claim 8,wherein the unknown sample is a different type of material than theparticular type of the material of interest.
 10. The non-transitorycomputer-readable medium of claim 8, wherein the unknown sample is theparticular type of the material of interest and is an incorrectlyobtained measurement.
 11. The non-transitory computer-readable medium ofclaim 8, wherein the metric and the corresponding metrics are decisionvalues.
 12. The non-transitory computer-readable medium of claim 8,wherein the threshold is a threshold quantity of standard deviations ofthe metric from an average of the corresponding metrics.
 13. Thenon-transitory computer-readable medium of claim 8, wherein the metricand the corresponding metrics are determined using a single classsupport vector machine technique.
 14. The non-transitorycomputer-readable medium of claim 8, wherein the quantification model isa local model, wherein the one or more instructions, when executed bythe one or more processors, further cause the one or more processors to:perform a first determination relating to the particular spectroscopicmeasurement of the unknown sample using a global model relating to theparticular type of the material of interest; generate the local modelbased on a particular result of the first determination and using anin-situ local modeling technique; and wherein the one or moreinstructions, that cause the one or more processors to obtain thequantification model, cause the one or more processors to: obtain thequantification model based on generating the local model.
 15. A method,comprising: receiving, by a device, information identifying results of anear infrared (NIR) spectroscopic measurement performed on an unknownsample; determining, by the device, a decision boundary for aquantification model, wherein the decision boundary divides a singleclass of the quantification model into a first plurality of training setsamples of the quantification model within the decision boundary and asecond plurality of training set samples of the quantification model isnot within the decision boundary; determining, by the device, that aparticular distance metric for the NIR spectroscopic measurementperformed on the unknown sample satisfies a threshold relative to otherdistance metrics for the second plurality of training set samples; andproviding, by the device, information indicating that the NIRspectroscopic measurement performed on the unknown sample does notcorrespond to the quantification model based on determining that theparticular distance metric for the NIR spectroscopic measurementperformed on the unknown sample satisfies the threshold relative to theother distance metrics for the second plurality of training set samples.16. The method of claim 15, further comprising: determining a type ofthe unknown sample based on the NIR spectroscopic measurement using aclassification model and based on determining that the particulardistance metric performed on the unknown sample satisfies the thresholdrelative to the other distance metrics for the second plurality oftraining set samples; and providing information identifying the type ofthe unknown sample.
 17. The method of claim 15, further comprising:determining the decision boundary based on a kernel function.
 18. Themethod of claim 17, wherein the kernel function is at least one of: aradial basis function, a polynomial function, a linear function, or anexponential function.
 19. The method of claim 15, wherein the thresholdis greater than at least one of: 1 standard deviation, 2 standarddeviations, or 3 standard deviations.
 20. The method of claim 15,wherein the first plurality of training set samples and the secondplurality of training set samples are associated with a set ofconcentrations of a component, and wherein each concentration of thecomponent, of the set of concentrations of the component, is associatedwith a threshold quantity of training set samples of the first pluralityof training set samples and the second plurality of training setsamples.